Forest Cover Classification with SVM

In this week’s lab we are exploring the use of Support Vector Machines for multi-class classification. Specifically, you will be using cartographic variables to predict forest cover type (7 types).

Natural resource managers responsible for developing ecosystem management strategies require basic descriptive information including inventory data like forest cover type for forested lands to support their decision-making processes. However, managers generally do not have this type of data for in-holdings or neighboring lands that are outside their immediate jurisdiction. One method of obtaining this information is through the use of predictive models.

You task is build both an SVM and a random forest model and compare their performance on accuracy and computation time.

  1. The data is available here: https://ucsb.box.com/s/ai5ost029enlguqyyn04bnlfaqmp8kn4

Explore the data.

Hint: Pay special attention to the distribution of the outcome variable across the classes.

  1. Create the recipe and carry out any necessary preprocessing. Can you use the same recipe for both models?

  2. Create the folds for cross-validation.

  3. Tune the models. Choose appropriate parameters and grids. If the computational costs of tuning given your strategy are prohibitive, how might you work around this?

  4. Conduct final predictions for both models and compare their prediction performances and computation costs from part 4.

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